1. Visual Analysis of Scientific Discoveries and Knowledge Diffusion Chaomei Chen 1,2 1 College of Information Science and Technology, Drexel University 2 WISELAB, Dalian University of Technology Email: chaomei.chen@drexel.edu

2. Outline 1.Introduction – Visual analytics – Motivation of the work – Grand challenges 2.The nature of insight – A recurring theme – A mechanism of discovery 3.An explanatory theory – Principles Computational properties – Examples Scientific discovery Complex network analysis Knowledge diffusion and information foraging 4.Conclusions

3. Visual Analytics The aim is to support analytical reasoning activities. – Detect the expected – Discover the unexpected * How does it differ from traditional information visualization? – Insight – Actionability – Example Air traffic control versus information search – Visual analytics emphasizes goal-driven problem solving

5. Analytical Reasoning Analyze a network – Decompose a graph into various components – Identify the nature of individual components and how they are interconnected – Interpret and make sense of what is visualized Categorization, aggregation

6. Scientific discoveries and knowledge diffusion It is not enough to show what they look like. One has to explain why they are structured and behaved as they are. Example – finding emerging trends and hot topics in scientific literature

8. Large-scale and periodical research assessment and evaluation in the UK, Australia, … Young researchers joining in industrial players such as Thomson Reuters

9. Grand Challenge 1: Maintaining a Long-Term Focus on Quality 1.Focus on quality in a longer term – Understand the nature of transformative discoveries in science – Understand factors that influence the diffusion of scientific knowledge – Improve our ability to evaluate the significance of scientific discoveries

10. Grand Challenge 2: Babylon chaos Understand trends and patterns of scientific change across different fields of studies of science Historical Philosophical Sociological Statistical Mathematical Literature-based Citation-based Now different theories are different theories

11. Grand Challenge 3: Social Computing The overwhelming rate and volume of data collected and stored The need for making sense of multi-source, heterogeneous data from multiple perspectives The need for communication across disciplines and professions Enabling techniques and data should be made to accessible to everyone – Analysts, scholars, scientists, policy makers, tax payers – Routinely and repeatedly analyze and synthesize – Social and cyber-enabled sense making Examples: – CiteULike – ManyEyes – People to Patent – Amazon book reviews – SkyWalker

12. Grand Challenge 4: Tightly Couple Science and Studies of Science The majority of scientometric, bibliometric, and informetric studies are inward-looking. – Few have set their goal to influence scientists directly! Exceptional Examples: – Literature-based discovery – Cyber-enabled discovery

13. Motivations What do creative ideas have in common? – Sociologists: Structural properties in social networks – Philosophers: Involving conflicting and competing views – Biomedical Scientists: They are quickly recognized and time tested Are there similar properties in scientific networks? Will they help us detect transformative discoveries in science? Will they help us explain why and how these discoveries are special?

14. SDSS

20. Chen, C., Zhang, J., Vogeley, M. S. (2009) Mapping the global impact of Sloan Digital Sky Survey. IEEE Intelligent Systems, 24(4), 74-77.

22. Patterns of Growth …

25. Aha!

37. MalloneeS_1996 has a burst rate of 3.8057, centrality of 0.70, and sigma of 0.72. The two citation peaks correspond to the Oklahoma City Bombing and the September 11 Terrorist Attacks. 1997 2001

39. WassermanS_1994 has a period of citation burst between 1998 and 2000. 1998 2000

40. Nobel Prize Winner: Gene Targeting A Sticky Effect

43. Next Now we know what structural and temporal properties are potentially good. And we’d be better off to think outside the box if we want to be creative. The next question: – What can we do to facilitate thinking outside the box? – In other words, how do we increase the actionability in various stages of the process?

44. Boundary Objects How communications can take place effectively among participants who have heterogeneous perspectives, trainings, and preferences? – Typically, scientists in interdisciplinary collaboration often find themselves in such situations. Boundary objects are – stable enough to maintain its own identify during the course of communication – volatile enough to preserve rooms for imagination A map is a good example of a boundary object ： – many layers of information – leaves much room for exploration from a wide range of different perspectives It is the freedom of instantiating ones’ own interpretations that facilitates communications between participants who may not have a clear understanding of the other side.

46. 4. Conclusions 4 grand challenges – Focus on quality – Babylon chaos – Social computing – Tight coupling 2 principles – Structural – Temporal 1 intermediate – Boundary objects In summary, visual analysis requires a different perspective to identify what is important and what to do with it.

47. Acknowledgements Drexel University, my colleagues and my graduate research assistants, especially James Zhang and Don Pellegrino NSF for funding the research Chinese Change Jiang Scholar Program and the WISELab at Dalian University, China Thomson Reuters